FIA - NLS Models: Biomass vs. Stand Age, G-Reconciled
JAH
2022-10-27
Background
This document has nls (non-linear least squares) regression fits using the LOG-NORMAL functional form to USFS FIA (United States Forest Service Forest Inventory & Analysis) biomass vs. stand age relationships. We calculated the biomass of each FIA plot by summing alive tree biomass (as reported by FIA). Stand age is also reported by FIA, using tree-core age estimates from two trees from the dominant size class of the FIA plot.
We considered the following Log-Normal functional form \(B = (1 + (yr-1990)* ge/100) \times (1 - \alpha \cdot B_l) \times (1 + \phi \cdot \Delta PDSI) \times \left(a + b \cdot \exp{ - \left[ \frac{ \log \left(StdAge_{t2} /c \right)} {d} \right]} ^2 \right)\), where \(B\) is the plot biomass, \(B_l\) is the calculated biomass loss (proportion) for the previous FIA plot census interval, \(STDAGE_{t2}\) is the stand age at the second of two FIA plot tree censuses, \(\Delta PDSI\) is the difference in the growing season (January-August) annual average PDSI values over the FIA plot biomass interval, which is defined as the measurement time minus 10 years and a 30-year climate normal from 1960-1989, and \(yr\) is the measurement year (all FIA data). Free parameters are \(ge\): biomass growth enhancement over time, \(\alpha\): the growth compensation of lost plot biomass, \(a\): the y-intercept of the curve, \(a +b\): the peak value of \(B\), \(c\): the \(StdAge\) value at peak \(B\), and \(d\): the log-normal curve shape parameter.
Model selection is used to determine the best fitting models including \(\phi\): the effect of changing climate (quantified as \(\Delta PDSI\), or the difference in the Palmer drought severity index from June - August for the 10 years preceding the biomass measurement and the 1960-1989 period) and \(\alpha\): the biomass compensation effect due to lost biomass (natural mortality or harvest).
model 1: simple model \(B = (1 + (yr-1990)* ge/100) \times \left(a + b \cdot \exp{ - \left[ \frac{ \log \left(StdAge_{t2} /c \right)} {d} \right]} ^2 \right)\)
model 2: phi model \(B = (1 + (yr-1990)* ge/100) \times (1 + \phi \cdot \Delta PDSI) \times \left(a + b \cdot \exp{ - \left[ \frac{ \log \left(StdAge_{t2} /c \right)} {d} \right]} ^2 \right)\)
model 3: phi-alpha model \(B = (1 + (yr-1990)* ge/100) \times (1 + \phi \cdot \Delta PDSI) \times (1 - \alpha \cdot B_l) \times \left(a + b \cdot \exp{ - \left[ \frac{ \log \left(StdAge_{t2} /c \right)} {d} \right]} ^2 \right)\)
Note:
This analysis only uses plot biomass data from the same plot locations and measurement intervals for which we also have data on biomass growth (which is used in the growth vs. biomass analysis ). We use the second of the two plot measurements comprising a \(G\) interval
This includes the following plot-based filtering criteria (which were used for the growth vs. biomass analysis):
- exclude FIA plots in plantation forests
- exclude FIA plots with multiple plot conditions (COND_PROP_UNADJ > 0.95)
- exclude FIA plots non-productive stands (i.e., those with less than 20 ft^3/acre/year timber producing capability; SITECLCD of 7)
- exclude FIA plots in non-stocked stands (i.e., those with STDSZCD of 5)
- exclude FIA plots in non-accessible areas (i.e., private lands etc., COND_STATUS_CD not equal to 1)
- exclude FIA plot visits that are not part of the annual inventories (which also includes FIA plot visits for Phase 3 ozone measurements)
Below the model fitting procedure is implemented by ecoprovince:
211 - Northeastern Mixed Forest
model selection 1
## Error in eval(extras, data, env) : object 'P_211' not found
## Error in eval(extras, data, env) : object 'P_211' not found
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 72677.43##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 5.614e-01 2.059e-01 2.726 0.00642 **
## phi 9.854e-03 6.256e-03 1.575 0.11528
## alpha 8.465e-01 2.725e-02 31.064 < 2e-16 ***
## a 4.030e+01 1.937e+00 20.805 < 2e-16 ***
## b 1.073e+02 5.001e+00 21.459 < 2e-16 ***
## c 1.144e+02 4.148e+00 27.588 < 2e-16 ***
## d 9.190e-01 3.966e-02 23.173 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4627 on 6798 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 18747 6234.1
## 2 18742 6234.1 5 0.04 0.0268 0.9997
## 3 18741 5853.7 1 380.34 1217.6643 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 195208.2
## 2 2 195169.6
## 3 3 193991.4##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 4.673e-01 1.141e-01 4.096 4.23e-05 ***
## phi 0.000e+00 3.595e-03 0.000 1
## alpha 7.596e-01 1.808e-02 42.006 < 2e-16 ***
## a 2.390e+01 7.237e-01 33.023 < 2e-16 ***
## b 8.004e+01 2.147e+00 37.277 < 2e-16 ***
## c 1.108e+02 3.013e+00 36.762 < 2e-16 ***
## d 1.189e+00 2.983e-02 39.855 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5589 on 18741 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (27 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7164 1238.0
## 2 7163 1238.0 1 0.00 0.000 1
## 3 7154 1100.4 9 137.59 99.388 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 78202.93
## 2 2 78204.93
## 3 3 77298.20##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.118e-01 1.271e-01 1.666 0.0957 .
## phi 0.000e+00 4.599e-03 0.000 1.0000
## alpha 8.317e-01 2.587e-02 32.154 <2e-16 ***
## a 3.054e+01 2.330e+00 13.106 <2e-16 ***
## b 1.625e+02 7.450e+00 21.811 <2e-16 ***
## c 1.347e+02 9.012e+00 14.946 <2e-16 ***
## d 1.333e+00 6.538e-02 20.387 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3922 on 7154 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (9 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 4868 1470.5
## 2 4867 1469.8 1 0.698 2.3127 0.1284
## 3 4860 1351.4 7 118.452 60.8552 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## model AIC
## 1 1 52441.03
## 2 2 52440.72
## 3 3 51990.44##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.046083 0.204593 -0.225 0.8218
## phi 0.022445 0.009502 2.362 0.0182 *
## alpha 0.888690 0.039183 22.681 <2e-16 ***
## a 26.487141 1.915801 13.826 <2e-16 ***
## b 120.168676 5.959752 20.163 <2e-16 ***
## c 107.013685 5.097928 20.992 <2e-16 ***
## d 1.053641 0.051099 20.620 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5273 on 4860 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (10 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.9567, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -31.414, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8771 1614.1
## 2 8770 1614.1 1 0.00 0.00 0.9999
## 3 8766 1487.3 4 126.79 186.82 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 92044.88
## 2 2 92046.88
## 3 3 91308.18##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 7.240e-02 1.103e-01 0.656 0.512
## phi 0.000e+00 5.223e-03 0.000 1.000
## alpha 7.809e-01 2.666e-02 29.292 <2e-16 ***
## a 3.121e+01 2.276e+00 13.712 <2e-16 ***
## b 1.074e+02 3.942e+00 27.247 <2e-16 ***
## c 1.078e+02 4.925e+00 21.882 <2e-16 ***
## d 1.264e+00 5.982e-02 21.138 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4119 on 8766 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (10 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 12326 3355.8
## 2 12325 3355.8 1 0.00 0.0 1
## 3 12324 2995.8 1 360.03 1481.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 133924.2
## 2 2 133926.2
## 3 3 132528.8##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.758e+00 1.507e-01 11.67 <2e-16 ***
## phi 0.000e+00 4.345e-03 0.00 1
## alpha 7.243e-01 1.419e-02 51.04 <2e-16 ***
## a 2.473e+01 7.808e-01 31.68 <2e-16 ***
## b 1.068e+02 3.870e+00 27.59 <2e-16 ***
## c 1.080e+02 6.116e+00 17.66 <2e-16 ***
## d 1.475e+00 4.881e-02 30.23 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.493 on 12324 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (16 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 12425 5068.8
## 2 12424 5065.8 1 3.00 7.3664 0.006655 **
## 3 12423 4612.3 1 453.42 1221.2549 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 137933.1
## 2 2 137927.8
## 3 3 136764.2##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.068e+00 1.492e-01 7.157 8.69e-13 ***
## phi 1.441e-02 5.553e-03 2.595 0.00948 **
## alpha 7.468e-01 1.525e-02 48.953 < 2e-16 ***
## a 2.835e+01 1.066e+00 26.591 < 2e-16 ***
## b 1.178e+02 5.361e+00 21.968 < 2e-16 ***
## c 1.222e+02 9.516e+00 12.842 < 2e-16 ***
## d 1.516e+00 6.354e-02 23.863 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6093 on 12423 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (40 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Error in nls(f_ln_3, data = G_234, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1258 341.03
## 2 1257 340.57 1 0.45741 1.6882 0.1941
## model AIC
## 1 1 14066.84
## 2 2 14067.14
## 3 3 NA##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b *
## exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 3.821e-02 3.664e-01 0.104 0.917
## a 1.252e+01 1.911e+01 0.655 0.512
## b 8.328e+02 2.810e+03 0.296 0.767
## c 5.000e+03 3.947e+04 0.127 0.899
## d 3.188e+00 3.021e+00 1.055 0.292
##
## Residual standard error: 0.5207 on 1258 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (2 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96304, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -11.433, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
## Error in nls(f_ln_1, data = G_242, start = c(ge = ge.start, a = a.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_2, data = G_242, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_3, data = G_242, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in get(paste("nls_242.", Mod.Sel1, sep = "")) :
## object 'nls_242.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1791 389.13
## 2 1790 389.12 1 0.015 0.0689 0.793
## 3 1789 367.65 1 21.464 104.4440 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18723.44
## 2 2 18725.38
## 3 3 18625.47##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.19924 0.32180 0.619 0.536
## phi 0.00000 0.01253 0.000 1.000
## alpha 0.74888 0.06801 11.011 < 2e-16 ***
## a 25.68778 4.97871 5.160 2.75e-07 ***
## b 100.74277 8.91977 11.294 < 2e-16 ***
## c 104.42177 8.31177 12.563 < 2e-16 ***
## d 1.14789 0.10986 10.449 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4533 on 1789 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96836, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -18.468, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 656 205.66
## 2 655 205.66 1 0.000 0.000 1
## 3 654 185.92 1 19.741 69.442 4.632e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6693.871
## 2 2 6695.871
## 3 3 6631.168##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.15981 0.38048 -0.420 0.675
## phi 0.00000 0.02663 0.000 1.000
## alpha 0.77294 0.08344 9.263 < 2e-16 ***
## a 20.69404 4.82816 4.286 2.09e-05 ***
## b 79.86186 9.27057 8.615 < 2e-16 ***
## c 63.52929 7.13466 8.904 < 2e-16 ***
## d 1.14819 0.15584 7.368 5.25e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5332 on 654 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (2 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94196, p-value = 2.149e-15
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -11.367, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
## Error in nls(f_ln_1, data = G_261, start = c(ge = ge.start, a = a.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_2, data = G_261, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_3, data = G_261, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_261$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in get(paste("nls_261.", Mod.Sel1, sep = "")) :
## object 'nls_261.' not foundsummary
simple model: does not fit
phi model: does not fit
phi-alpha model: does not fit
unable to fit model (only 64 observations)
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"262 - California Dry Steppe
model selection 1
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`", :
## missing value where TRUE/FALSE needed
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_262$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in get(paste("nls_262.", Mod.Sel1, sep = "")) :
## object 'nls_262.' not foundsummary
simple model: does not fit
phi model: does not fit
phi-alpha model: does not fit
unable to fit model (0 observations)
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
## Error in nls(f_ln_3, data = G_263, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: false convergence (8)
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 149 26.638
## 2 148 26.638 1 -3.6088e-11 0 1
## model AIC
## 1 1 1935.134
## 2 2 1937.134
## 3 3 NA##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b *
## exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 1.224 2.254 0.543 0.5879
## a 0.000 69.109 0.000 1.0000
## b 1000.000 913.184 1.095 0.2753
## c 1898.226 4726.971 0.402 0.6886
## d 2.777 1.518 1.830 0.0693 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4228 on 149 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: does not fit
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.97748, p-value = 0.0126
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -2.2016, p-value = 0.0277
## alternative hypothesis: two.sidedpredict and plot
plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
## Error in nls(f_ln_1, data = G_313, start = c(ge = ge.start, a = a.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_2, data = G_313, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_3, data = G_313, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in get(paste("nls_313.", Mod.Sel1, sep = "")) :
## object 'nls_313.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"- Cannot fit model
- not enough data (only 3 observations)
331 - Great Plains/Palouse Dry Steppe
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 298 169.14
## 2 297 169.14 1 0.0000 0.0000 1.0000
## 3 296 167.90 1 1.2399 2.1858 0.1404
## model AIC
## 1 1 3100.582
## 2 2 3102.582
## 3 3 3102.353##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b *
## exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.4889 1.4827 0.330 0.7418
## a 0.0000 19.2900 0.000 1.0000
## b 52.4290 27.5134 1.906 0.0577 .
## c 119.8342 66.7961 1.794 0.0738 .
## d 2.1713 1.2242 1.774 0.0771 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7534 on 298 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.8809, p-value = 1.318e-14
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.2814, p-value = 3.356e-10
## alternative hypothesis: two.sidedpredict and plot
plotting 2
* Cannot fit model
332 - Great Plains Steppe
model selection 1
## Error in nls(f_ln_1, data = G_332, start = c(ge = ge.start, a = a.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(f_ln_2, data = G_332, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(f_ln_3, data = G_332, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_332$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in get(paste("nls_332.", Mod.Sel1, sep = "")) :
## object 'nls_332.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"- model not fitted because only 62 observations
predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"342 - Intermountain Semi-Desert
model selection 1
## Error in nls(f_ln_1, data = G_342, start = c(ge = ge.start, a = a.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_2, data = G_342, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## Error in nls(f_ln_3, data = G_342, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: singular convergence (7)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 NA## Warning in min(AIC1_342$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf## Error in get(paste("nls_342.", Mod.Sel1, sep = "")) :
## object 'nls_342.' not foundsummary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"411 - Everglades
model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
## [1] "cannot plot observed vs. predicted"M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6721 1270.3
## 2 6720 1264.3 1 6.032 32.06 1.557e-08 ***
## 3 6719 1085.2 1 179.120 1109.04 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 71115.80
## 2 2 71085.79
## 3 3 70060.23##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 4.245e-01 1.530e-01 2.775 0.00554 **
## phi 2.541e-02 5.584e-03 4.550 5.45e-06 ***
## alpha 8.215e-01 2.232e-02 36.804 < 2e-16 ***
## a 1.751e+01 2.891e+00 6.057 1.47e-09 ***
## b 1.531e+02 1.030e+01 14.864 < 2e-16 ***
## c 1.961e+02 2.327e+01 8.430 < 2e-16 ***
## d 1.631e+00 1.078e-01 15.122 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4019 on 6719 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (3 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8027 1280.2
## 2 8026 1280.2 1 0.000 0.00 1
## 3 8025 1188.2 1 92.026 621.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 88546.92
## 2 2 88548.92
## 3 3 87951.76##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 9.518e-01 1.253e-01 7.595 3.44e-14 ***
## phi 0.000e+00 4.205e-03 0.000 1
## alpha 8.478e-01 3.179e-02 26.673 < 2e-16 ***
## a 3.887e+01 2.121e+00 18.330 < 2e-16 ***
## b 1.129e+02 3.539e+00 31.916 < 2e-16 ***
## c 9.991e+01 2.742e+00 36.436 < 2e-16 ***
## d 1.168e+00 4.351e-02 26.839 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3848 on 8025 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (2 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
predict and plot
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Error in nls(f_ln_1, data = G_M223, start = c(ge = ge.start, a = a.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(f_ln_2, data = G_M223, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 8818.569##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -6.054e-01 2.277e-01 -2.659 0.00798 **
## phi 7.662e-02 1.800e-02 4.257 2.3e-05 ***
## alpha 8.905e-01 7.124e-02 12.500 < 2e-16 ***
## a 0.000e+00 4.759e+01 0.000 1.00000
## b 3.440e+02 6.861e+02 0.501 0.61621
## c 1.654e+03 1.060e+04 0.156 0.87595
## d 3.294e+00 3.719e+00 0.886 0.37607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3373 on 875 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96709, p-value = 3.239e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -13.555, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Error in nls(f_ln_1, data = G_M231, start = c(ge = ge.start, a = a.start, :
## Convergence failure: iteration limit reached without convergence (10)
## Error in nls(f_ln_2, data = G_M231, start = c(ge = ge.start, phi = phi.start, :
## Convergence failure: iteration limit reached without convergence (10)
## model AIC
## 1 1 NA
## 2 2 NA
## 3 3 10131.35##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 5.135e-01 5.352e-01 0.959 0.3376
## phi 7.733e-03 2.242e-02 0.345 0.7302
## alpha 7.642e-01 7.743e-02 9.870 <2e-16 ***
## a 1.667e+01 8.120e+00 2.053 0.0404 *
## b 1.758e+02 1.157e+02 1.519 0.1290
## c 4.206e+02 6.048e+02 0.695 0.4869
## d 2.147e+00 8.579e-01 2.502 0.0125 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.453 on 980 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96207, p-value = 2.47e-15
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -13.989, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M242 - Cascade Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3172 1918.7
## 2 3171 1918.7 1 0.00 0.0000 1
## 3 3148 1790.5 23 128.11 9.7928 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 41988.35
## 2 2 41990.35
## 3 3 41548.04##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -0.18061 0.49156 -0.367 0.713331
## phi 0.00000 0.01505 0.000 1.000000
## alpha 1.10192 0.06873 16.033 < 2e-16 ***
## a 3.55424 13.98067 0.254 0.799338
## b 568.48059 92.86868 6.121 1.04e-09 ***
## c 667.30946 174.01443 3.835 0.000128 ***
## d 2.21626 0.22808 9.717 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7542 on 3148 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (24 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.95105, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -17.577, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1952 1001.42
## 2 1951 925.67 1 75.745 159.6441 < 2.2e-16 ***
## 3 1935 894.94 16 30.737 4.1537 5.856e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 24765.07
## 2 2 24613.15
## 3 3 24401.78summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91612, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.034, p-value = 0.002414
## alternative hypothesis: two.sidedpredict and plot
## Warning in mean.default(G_M261$MEASTIM, na.rm = TE): argument is not numeric or
## logical: returning NA## Warning: Removed 1000 row(s) containing missing values (geom_path).
plotting 2
## Warning: Removed 20 rows containing missing values (geom_point).## Warning: Removed 20 rows containing missing values (geom_pointrange).
M262 - California coastal range - coniferous forest - open woodland - shrub meadow
Model selection 1
summary
- simple model: does not fit
- phi model: does not fit
- phi-alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"- model can fit - but K is negative (only 19 observations) - model excluded
predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 356 110.33
## 2 355 109.15 1 1.1717 3.8108 0.051710 .
## 3 354 105.62 1 3.5361 11.8518 0.000645 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3771.802
## 2 2 3769.947
## 3 3 3760.059##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -1.81573 0.33677 -5.392 1.28e-07 ***
## phi 0.04237 0.02511 1.688 0.092361 .
## alpha 0.50499 0.13502 3.740 0.000214 ***
## a 54.76880 15.65852 3.498 0.000529 ***
## b 184.32447 40.04085 4.603 5.80e-06 ***
## c 158.85154 25.00796 6.352 6.53e-10 ***
## d 0.81683 0.15947 5.122 4.97e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5462 on 354 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94156, p-value = 9.985e-11
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.18153, p-value = 0.856
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1691 583.39
## 2 1690 583.09 1 0.308 0.8923 0.345
## 3 1689 527.98 1 55.108 176.2917 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 17699.64
## 2 2 17700.75
## 3 3 17534.37##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -1.07060 0.29359 -3.647 0.000274 ***
## phi 0.01782 0.01162 1.534 0.125224
## alpha 0.64315 0.04157 15.472 < 2e-16 ***
## a 41.57156 5.51472 7.538 7.73e-14 ***
## b 135.46943 16.29218 8.315 < 2e-16 ***
## c 212.14212 23.03599 9.209 < 2e-16 ***
## d 1.23436 0.11259 10.963 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5591 on 1689 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (15 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92964, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.9597, p-value = 7.058e-07
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2641 1154.9
## 2 2640 1151.5 1 3.413 7.8251 0.00519 **
## 3 2617 1078.3 23 73.212 7.7253 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 28864.48
## 2 2 28858.65
## 3 3 28506.38##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 0.73312 0.75925 0.966 0.33434
## phi 0.03805 0.01345 2.828 0.00472 **
## alpha 0.63197 0.04647 13.598 < 2e-16 ***
## a 25.71790 4.64424 5.538 3.37e-08 ***
## b 99.70475 17.65984 5.646 1.82e-08 ***
## c 210.36985 18.10340 11.620 < 2e-16 ***
## d 1.24064 0.09401 13.197 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6419 on 2617 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (25 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89938, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.6907, p-value = 2.722e-06
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1665 654.15
## 2 1664 653.64 1 0.511 1.3008 0.2542
## 3 1663 607.37 1 46.266 126.6784 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 18742.43
## 2 2 18743.13
## 3 3 18622.53##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge 2.49946 1.96354 1.273 0.20322
## phi 0.01486 0.01732 0.858 0.39089
## alpha 0.70768 0.05537 12.781 < 2e-16 ***
## a 17.72088 5.65869 3.132 0.00177 **
## b 88.45316 27.94636 3.165 0.00158 **
## c 132.83193 5.59014 23.762 < 2e-16 ***
## d 0.97930 0.04976 19.680 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6043 on 1663 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (5 observations deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93649, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.1382, p-value = 3.5e-05
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 356 147.67
## 2 355 146.35 1 1.3214 3.2053 0.07425 .
## 3 354 127.38 1 18.9706 52.7223 2.459e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3622.910
## 2 2 3621.665
## 3 3 3573.547##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -1.030e+00 4.880e-01 -2.111 0.0355 *
## phi 5.401e-03 3.136e-02 0.172 0.8633
## alpha 7.993e-01 9.457e-02 8.452 7.54e-16 ***
## a 0.000e+00 7.695e+01 0.000 1.0000
## b 1.379e+02 2.192e+02 0.629 0.5295
## c 7.728e+02 4.021e+03 0.192 0.8477
## d 3.138e+00 4.935e+00 0.636 0.5253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5999 on 354 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94196, p-value = 1.11e-10
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.6772, p-value = 2.436e-11
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 2: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Model 3: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi * DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 207 64.472
## 2 206 64.472 1 0.0000 0.000 0.9999985
## 3 205 60.643 1 3.8286 12.942 0.0004027 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 2088.502
## 2 2 2090.502
## 3 3 2079.523##
## Formula: B_plt_t2_MgHa ~ (1 + (MEASTIME_t2 - 1990) * ge/100) * (1 + phi *
## DeltaPDSI) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t2/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## ge -1.88598 0.37590 -5.017 1.13e-06 ***
## phi 0.00000 0.05799 0.000 1.000000
## alpha 0.50427 0.12433 4.056 7.10e-05 ***
## a 36.45861 9.41288 3.873 0.000144 ***
## b 126.50101 26.04280 4.857 2.36e-06 ***
## c 150.15616 16.92871 8.870 3.62e-16 ***
## d 0.93149 0.16914 5.507 1.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5439 on 205 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1 observation deleted due to missingness)summary
- simple model: fits
- phi model: fits
- phi-alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93284, p-value = 2.735e-08
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -0.89551, p-value = 0.3705
## alternative hypothesis: two.sidedpredict and plot
plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 3 |
| 212 | Laurentian Mixed Forest | 3 |
| 221 | Eastern Broadleaf Forest | 3 |
| 222 | Midwest Broadleaf Forest | 3 |
| 223 | Central Interior Broadleaf Forest | 3 |
| 231 | Southeastern Mixed Forest | 3 |
| 232 | Outer Coastal Plain Mixed Forest | 3 |
| 234 | Lower Mississippi Riverine Forest | 1 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 3 |
| 255 | Prairie Parkland (Subtropical) | 3 |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | 1 |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | 1 |
| 332 | Great Plains Steppe | NA |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 3 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 3 |
| M223 | Ozark Broadleaf Forest Meadow | 3 |
| M231 | Ouachita Mixed Forest | 3 |
| M242 | Cascade Mixed Forest | 3 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 3 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | 3 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | 3 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 3 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 3 |
| M334 | Black Hills Coniferous Forest | 3 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | 3 |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | ge | ge.2.5 | ge.97.5 | phi | phi.2.5 | phi.97.5 | alpha | alpha.2.5 | alpha.97.5 | a | a.2.5 | a.97.5 | b | b.2.5 | b.97.5 | c | c.2.5 | c.97.5 | d | d.2.5 | d.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 6806 | 2847 | 0.5614145 | 0.1577549 | 0.9650741 | 0.0098539 | -0.0024099 | 0.0221177 | 0.8464830 | 0.7930648 | 0.8999011 | 40.304388 | 36.5067722 | 44.10200 | 107.31646 | 97.512853 | 117.12006 | 114.42661 | 106.29574 | 122.55748 | 0.9189999 | 0.8412571 | 0.9967427 |
| 212 | Laurentian Mixed Forest | east | 18775 | 8891 | 0.4673302 | 0.2436774 | 0.6909831 | 0.0000000 | -0.0070458 | 0.0070458 | 0.7596243 | 0.7241785 | 0.7950701 | 23.899941 | 22.4813449 | 25.31854 | 80.03947 | 75.830899 | 84.24804 | 110.76006 | 104.85445 | 116.66568 | 1.1888448 | 1.1303764 | 1.2473132 |
| 221 | Eastern Broadleaf Forest | east | 7170 | 3490 | 0.2118160 | -0.0373764 | 0.4610084 | 0.0000000 | -0.0090147 | 0.0090147 | 0.8317337 | 0.7810259 | 0.8824414 | 30.540702 | 25.9727555 | 35.10865 | 162.50411 | 147.899022 | 177.10920 | 134.68414 | 117.01892 | 152.34937 | 1.3329128 | 1.2047471 | 1.4610786 |
| 222 | Midwest Broadleaf Forest | east | 4877 | 2401 | -0.0460834 | -0.4471784 | 0.3550117 | 0.0224450 | 0.0038175 | 0.0410725 | 0.8886905 | 0.8118750 | 0.9655059 | 26.487141 | 22.7313038 | 30.24298 | 120.16868 | 108.484868 | 131.85248 | 107.01369 | 97.01944 | 117.00793 | 1.0536407 | 0.9534643 | 1.1538171 |
| 223 | Central Interior Broadleaf Forest | east | 8783 | 3725 | 0.0724004 | -0.1438177 | 0.2886185 | 0.0000000 | -0.0102390 | 0.0102390 | 0.7809249 | 0.7286645 | 0.8331854 | 31.207745 | 26.7462045 | 35.66929 | 107.40334 | 99.676437 | 115.13025 | 107.77584 | 98.12105 | 117.43062 | 1.2644153 | 1.1471617 | 1.3816690 |
| 231 | Southeastern Mixed Forest | east | 12347 | 5691 | 1.7584838 | 1.4630660 | 2.0539017 | 0.0000000 | -0.0085177 | 0.0085177 | 0.7243199 | 0.6965018 | 0.7521381 | 24.733949 | 23.2034139 | 26.26448 | 106.78132 | 99.195302 | 114.36733 | 107.97366 | 95.98608 | 119.96123 | 1.4752723 | 1.3795973 | 1.5709472 |
| 232 | Outer Coastal Plain Mixed Forest | east | 12470 | 6101 | 1.0680057 | 0.7755160 | 1.3604953 | 0.0144070 | 0.0035228 | 0.0252912 | 0.7467943 | 0.7168913 | 0.7766972 | 28.350008 | 26.2601723 | 30.43984 | 117.77229 | 107.263792 | 128.28078 | 122.20351 | 103.55149 | 140.85553 | 1.5162934 | 1.3917433 | 1.6408436 |
| 234 | Lower Mississippi Riverine Forest | east | 1265 | 714 | 0.0382065 | -0.6806976 | 0.7571107 | NA | NA | NA | NA | NA | NA | 12.524348 | -24.9680876 | 50.01678 | 832.84146 | -4680.032010 | 6345.71492 | 5000.00000 | -72429.65447 | 82429.65447 | 3.1877269 | -2.7399232 | 9.1153769 |
| 242 | Pacific Lowland Mixed Forest | pacific | 81 | 81 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 1797 | 809 | 0.1992410 | -0.4319029 | 0.8303849 | 0.0000000 | -0.0245663 | 0.0245663 | 0.7488833 | 0.6154953 | 0.8822713 | 25.687780 | 15.9230754 | 35.45248 | 100.74277 | 83.248507 | 118.23703 | 104.42177 | 88.11996 | 120.72357 | 1.1478928 | 0.9324216 | 1.3633639 |
| 255 | Prairie Parkland (Subtropical) | pacific | 663 | 293 | -0.1598090 | -0.9069202 | 0.5873022 | 0.0000000 | -0.0522822 | 0.0522822 | 0.7729398 | 0.6090928 | 0.9367868 | 20.694043 | 11.2134875 | 30.17460 | 79.86186 | 61.658187 | 98.06554 | 63.52929 | 49.51970 | 77.53889 | 1.1481892 | 0.8421804 | 1.4541980 |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 24 | 24 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 155 | 155 | 1.2239516 | -3.2300182 | 5.6779213 | NA | NA | NA | NA | NA | NA | 0.000000 | -136.5600679 | 136.56007 | 1000.00000 | -804.464574 | 2804.46457 | 1898.22617 | -7442.33100 | 11238.78334 | 2.7771137 | -0.2216429 | 5.7758703 |
| 313 | Colorado Plateau Semi-Desert | interior west | 215 | 215 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 4 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 9 | 9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 3 | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 304 | 240 | 0.4888930 | -2.4290839 | 3.4068698 | NA | NA | NA | NA | NA | NA | 0.000000 | -37.9618826 | 37.96188 | 52.42899 | -1.716234 | 106.57421 | 119.83421 | -11.61767 | 251.28608 | 2.1713420 | -0.2377897 | 4.5804737 |
| 332 | Great Plains Steppe | interior west | 195 | 106 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 341 | Intermountain Semi-Desert and Desert | interior west | 62 | 62 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 121 | 120 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 93 | 61 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 6729 | 2989 | 0.4244549 | 0.1245744 | 0.7243355 | 0.0254110 | 0.0144641 | 0.0363579 | 0.8214671 | 0.7777125 | 0.8652217 | 17.506687 | 11.8403129 | 23.17306 | 153.07530 | 132.887777 | 173.26283 | 196.14427 | 150.53438 | 241.75416 | 1.6306139 | 1.4192376 | 1.8419902 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 8034 | 3700 | 0.9517694 | 0.7061059 | 1.1974328 | 0.0000000 | -0.0082433 | 0.0082433 | 0.8478212 | 0.7855128 | 0.9101296 | 38.871490 | 34.7143586 | 43.02862 | 112.93446 | 105.998009 | 119.87091 | 99.91458 | 94.53922 | 105.28995 | 1.1678540 | 1.0825568 | 1.2531512 |
| M223 | Ozark Broadleaf Forest Meadow | east | 883 | 343 | -0.6053958 | -1.0522432 | -0.1585484 | 0.0766208 | 0.0412940 | 0.1119475 | 0.8905107 | 0.7506879 | 1.0303336 | 0.000000 | -93.3960670 | 93.39607 | 343.99698 | -1002.514773 | 1690.50873 | 1654.41165 | -19140.77724 | 22449.60055 | 3.2937215 | -4.0057665 | 10.5932095 |
| M231 | Ouachita Mixed Forest | east | 988 | 481 | 0.5134884 | -0.5368625 | 1.5638393 | 0.0077329 | -0.0362649 | 0.0517307 | 0.7642262 | 0.6122847 | 0.9161677 | 16.669136 | 0.7341976 | 32.60408 | 175.75451 | -51.246800 | 402.75582 | 420.56722 | -766.18859 | 1607.32303 | 2.1467388 | 0.4631696 | 3.8303080 |
| M242 | Cascade Mixed Forest | pacific | 3179 | 3176 | -0.1806076 | -1.1444145 | 0.7831992 | 0.0000000 | -0.0295146 | 0.0295146 | 1.1019213 | 0.9671674 | 1.2366752 | 3.554237 | -23.8579198 | 30.96639 | 568.48059 | 386.391317 | 750.56986 | 667.30946 | 326.11626 | 1008.50266 | 2.2162649 | 1.7690691 | 2.6634607 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 1963 | 1963 | 0.0592276 | -1.1060519 | 1.2245071 | 0.1887636 | 0.1653016 | 0.2122257 | 0.7151681 | 0.5373863 | 0.8929498 | 0.000000 | -49.7841398 | 49.78414 | 505.48187 | 249.528689 | 761.43505 | 905.74901 | -314.98988 | 2126.48791 | 2.7254405 | 1.6285474 | 3.8223335 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 19 | 19 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 362 | 362 | -1.8157315 | -2.4780584 | -1.1534046 | 0.0423700 | -0.0070057 | 0.0917456 | 0.5049877 | 0.2394459 | 0.7705295 | 54.768802 | 23.9733782 | 85.56423 | 184.32447 | 105.576614 | 263.07233 | 158.85154 | 109.66869 | 208.03438 | 0.8168324 | 0.5032028 | 1.1304621 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 1711 | 1711 | -1.0705976 | -1.6464275 | -0.4947678 | 0.0178205 | -0.0049652 | 0.0406063 | 0.6431528 | 0.5616230 | 0.7246825 | 41.571559 | 30.7551625 | 52.38796 | 135.46943 | 103.514440 | 167.42443 | 212.14212 | 166.96002 | 257.32422 | 1.2343633 | 1.0135251 | 1.4552014 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 2649 | 2648 | 0.7331242 | -0.7556754 | 2.2219239 | 0.0380474 | 0.0116674 | 0.0644274 | 0.6319656 | 0.5408357 | 0.7230954 | 25.717902 | 16.6111486 | 34.82465 | 99.70475 | 65.076091 | 134.33342 | 210.36985 | 174.87141 | 245.86829 | 1.2406376 | 1.0563039 | 1.4249713 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 1675 | 1675 | 2.4994612 | -1.3518126 | 6.3507349 | 0.0148648 | -0.0191073 | 0.0488368 | 0.7076774 | 0.5990745 | 0.8162802 | 17.720879 | 6.6219799 | 28.81978 | 88.45316 | 33.639406 | 143.26692 | 132.83193 | 121.86747 | 143.79639 | 0.9793045 | 0.8817043 | 1.0769048 |
| M334 | Black Hills Coniferous Forest | interior west | 362 | 170 | -1.0302125 | -1.9899535 | -0.0704714 | 0.0054010 | -0.0562644 | 0.0670664 | 0.7993264 | 0.6133402 | 0.9853125 | 0.000000 | -151.3293796 | 151.32938 | 137.94375 | -293.146235 | 569.03374 | 772.76960 | -7135.30221 | 8680.84140 | 3.1376453 | -6.5672504 | 12.8425411 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 213 | 213 | -1.8859784 | -2.6271118 | -1.1448450 | 0.0000000 | -0.1143395 | 0.1143395 | 0.5042656 | 0.2591273 | 0.7494039 | 36.458606 | 17.9001475 | 55.01706 | 126.50101 | 75.154935 | 177.84709 | 150.15616 | 116.77947 | 183.53286 | 0.9314925 | 0.5980113 | 1.2649737 |
plot ge
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I
map #2
plot phi (effect of DeltaPDSI)
plot alpha (biomass compensation effect)
plot a coefficient
## Warning: Removed 15 rows containing missing values (geom_point).
plot b coefficient
## Warning: Removed 12 rows containing missing values (geom_point).
plot c coefficient
## Warning: Removed 15 rows containing missing values (geom_point).
plot d coefficient
## Warning: Removed 12 rows containing missing values (geom_point).
Caclulations - weighted averages
ge (stand biomass enhancement factor in % 2000-2021)
## region weighted.ge
## 1 entire US 0.55968415
## 2 pacific -0.05524554
## 3 east 0.66740389
## 4 interior west 0.42071032phi (effect of DeltaPDSI)
## region weighted.phi
## 1 entire US 0.014754084
## 2 pacific 0.065098909
## 3 east 0.006528787
## 4 interior west 0.022812632alpha (biomass compensation effect)
## region weighted.alpha
## 1 entire US 0.7600027
## 2 pacific 0.9012734
## 3 east 0.7731147
## 4 interior west 0.5803015Calculations - weighted averages subsetted to 15 ecoprovinces
- 211, 212, 221, 223, 231, 232, 234, 251, M211, M221, M223, M231, M242, M261, M332
ge
## region weighted.ge
## 1 entire US 0.62621417
## 2 pacific -0.08899515
## 3 east 0.71149013
## 4 interior west 0.73312423phi
## region weighted.phi
## 1 entire US 0.014572771
## 2 pacific 0.072104104
## 3 east 0.005578167
## 4 interior west 0.038047400alpha
## region weighted.alpha
## 1 entire US 0.7799773
## 2 pacific 0.9541889
## 3 east 0.7673245
## 4 interior west 0.6319656